refactor: Update README to reflect new Agentic RAG app with Embedding Gemma and remove outdated agent files

README.md

@@ -140,7 +140,7 @@ A curated collection of **Awesome LLM apps built with RAG, AI Agents, Multi-agen
 *   [🌍 AI Travel Planner MCP Agent](mcp_ai_agents/ai_travel_planner_mcp_agent_team)
 
 ### 📀 RAG (Retrieval Augmented Generation)
-*   [🔗 Agentic RAG](rag_tutorials/agentic_rag/)
+*   [🔗 Agentic RAG with Embedding Gemma](rag_tutorials/agentic_rag_embedding_gemma)
 *   [🧐 Agentic RAG with Reasoning](rag_tutorials/agentic_rag_with_reasoning/)
 *   [📰 AI Blog Search (RAG)](rag_tutorials/ai_blog_search/)
 *   [🔍 Autonomous RAG](rag_tutorials/autonomous_rag/)

rag_tutorials/agentic_rag/README.md

@@ -1,46 +0,0 @@
-## 🗃️ AI RAG Agent with Web Access 
-This script demonstrates how to build a Retrieval-Augmented Generation (RAG) agent with web access using GPT-4o in just 15 lines of Python code. The agent uses a PDF knowledge base and has the ability to search the web using DuckDuckGo.
-
-### Features
-
-- Creates a RAG agent using GPT-4o
-- Incorporates a PDF-based knowledge base
-- Uses LanceDB as the vector database for efficient similarity search
-- Includes web search capability through DuckDuckGo
-- Provides a playground interface for easy interaction
-
-### How to get Started?
-
-1. Clone the GitHub repository
-```bash
-git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
-cd awesome-llm-apps/rag_tutorials/agentic_rag
-```
-
-2. Install the required dependencies:
-
-```bash
-pip install -r requirements.txt
-```
-
-3. Get your OpenAI API Key
-
-- Sign up for an [OpenAI account](https://platform.openai.com/) (or the LLM provider of your choice) and obtain your API key.
-- Set your OpenAI API key as an environment variable:
-```bash
-export OPENAI_API_KEY='your-api-key-here'
-```
-
-4. Run the AI RAG Agent 
-```bash
-python3 rag_agent.py
-```
-5. Open your web browser and navigate to the URL provided in the console output to interact with the RAG agent through the playground interface.
-
-### How it works?
-
-1. **Knowledge Base Creation:** The script creates a knowledge base from a PDF file hosted online.
-2. **Vector Database Setup:** LanceDB is used as the vector database for efficient similarity search within the knowledge base.
-3. **Agent Configuration:** An AI agent is created using GPT-4o as the underlying model, with the PDF knowledge base and DuckDuckGo search tool.
-4. **Playground Setup:** A playground interface is set up for easy interaction with the RAG agent.
-

rag_tutorials/agentic_rag/rag_agent.py

@@ -1,30 +0,0 @@
-from agno.agent import Agent
-from agno.models.openai import OpenAIChat
-from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
-from agno.vectordb.lancedb import LanceDb, SearchType
-from agno.playground import Playground, serve_playground_app
-from agno.tools.duckduckgo import DuckDuckGoTools
-
-db_uri = "tmp/lancedb"
-# Create a knowledge base from a PDF
-knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
-    # Use LanceDB as the vector database
-    vector_db=LanceDb(table_name="recipes", uri=db_uri, search_type=SearchType.vector),
-)
-# Load the knowledge base: Comment out after first run
-knowledge_base.load(upsert=True)
-
-rag_agent = Agent(
-    model=OpenAIChat(id="gpt-4o"),
-    agent_id="rag-agent",
-    knowledge=knowledge_base, # Add the knowledge base to the agent
-    tools=[DuckDuckGoTools()],
-    show_tool_calls=True,
-    markdown=True,
-)
-
-app = Playground(agents=[rag_agent]).get_app()
-
-if __name__ == "__main__":
-    serve_playground_app("rag_agent:app", reload=True)

rag_tutorials/agentic_rag/requirements.txt

@@ -1,8 +0,0 @@
-agno
-openai
-lancedb 
-tantivy 
-pypdf 
-sqlalchemy 
-pgvector 
-psycopg[binary]